RDI
Healthcare
ClinicalDataManager-DataIntegrity&Investigation
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“Clinical Data Manager - Data Integrity & Investigation at RDI. Skills: Data integrity, Investigation, Root cause analysis. Own subject- and sample-level data. Make sure dataset tells the truth”
What You'll Achieve.
Get better tests to market faster
Industry & Context.
Investigate discordant results; Find root cause; Troubleshoot data discrepancies
What They're Looking For.
Must Have
3+ years clinical data management, Hands-on EDC experience, Enough biostatistical literacy
Nice to Have
IVD/diagnostics experience a plus, FDA submission exposure, GCP/ICH familiarity
What You'll Do.
Own subject- and sample-level data
Make sure dataset tells the truth
Chase every anomaly to root cause
Trace discordant results
Trace drift to source
Determine real signal or data artifact
Trace critical field to source
Find single transcription error
Ensure lab results agree
Ensure sample inventory agree
Ensure demographics agree
Ensure visit dates agree
Explain what data means
Hold the line on data story
Accountable for dataset for FDA
Defend every value under audit
Catch bad data at entry
Set standard for clean data
How You'll Work.
Team & Collaboration
Sit between clinical, lab, regulatory, engineering
Communication Scope
Explain what data means; Hold the line
Full Job Description
**Clinical Data Manager — Data Integrity & Investigation** _Own the data. Find the true story it's hiding._ **About RDI** RDI is a CRO purpose-built for diagnostics. Since 2008 we've run 300+ IVD trials for the largest test makers in the world. Our job is to get better tests to market faster — with cleaner data, smarter tools, and zero nonsense. **The Role** When a new assay disagrees with the predicate, somebody has to figure out _why_. Is it the test? The specimen? The site? A single keystroke at data entry? Most "data people" report the discordance and move on. We're looking for the one who treats it as a case to crack. You'll own subject- and sample-level data across 20–30 concurrent method-comparison and specimen-collection studies — and your real job is to make sure the dataset tells the truth. Not the convenient version. Not the version that makes a result look cleaner than it is. What actually happened. You check every cell because the truth lives in the cells, and you chase every anomaly to root cause because "looks off" is where the work _starts_. **What this role actually is** * **Investigation** — you trace discordant results, outliers, and drift to their source and determine whether you're looking at real signal or a data artifact. * **Source-level verification** — every critical field traced to origin; you're the one who finds the single transcription error in 10,000 rows. * **Reconciliation** — lab results, sample inventory, demographics, and visit dates agree across systems, or you find out why. * **Telling it straight** — you can stand in front of the science and regulatory team and explain what the data _means_ , honestly — and hold the line when someone wants a prettier story than the data supports. * **Database lock** — you're accountable for the dataset that goes to FDA, and you'll defend every value in it under audit. * **ALCOA+ / 21 CFR Part 11** — integrity isn't a checkbox here, it's the whole job. **How you'll work** * Dig directly into raw
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